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HFEPX · Eval paper review

LLM Validation Makes Minimal-Pair Benchmarks Easier for Language Models

Guofang Tang

Published

Nov 10, 2026

Citations

0

Trust level

Low

Usefulness score

37/100 (Low)

Extraction confidence

45% (Low)

Derived from extracted protocol signals and abstract evidence.

Rater population

Not reported

Signals refreshed

Nov 10, 2026

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Automatic Metrics Coding Human Eval

Should you rely on this paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Main weakness

The available metadata is too thin to trust this as a primary source.

Human feedback signal
Not explicit
Not explicit in abstract metadata
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
37/100
Adjacent candidate

Treat as adjacent context, not a core eval-method reference.

If you are doing eval pipeline work, start here

Human Eval Papers LLM-as-Judge Papers Pairwise Preference Papers Tool-Use Evaluation Search

Abstract

Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation. We test it on BLiMP (English) and QFrBLiMP (Quebec French), the two public minimal-pair datasets releasing every forced-choice human judgment. Three LLMs validate by forced choice, by rating, and as a panel, scored against human panels of one, three and five judges in two pre-registered phases. On BLiMP, forced-choice validators agree with held-out human majorities more often than five-judge panels, yet catch far fewer pairs whose label humans reject than one judge does. By point estimate, no validator is worth more than one judge. The errors are not random. On both datasets, across 10 open LMs, human-supported pairs that the pre-registered LLM filter flags are 18.9 points harder than those it accepts, in all 70 LM-validator combinations, and harder than pairs a single human judge flags. Filtering thus removes items on which LMs fail but humans do not, raising accuracy more than random removal. A protocol frozen before any output replicates the gap and the lift on Dutch BLiMP-NL. LLM validation makes minimal-pair benchmarks easier for the models they are meant to test; we release code, validator outputs and item-level scores.

What we could verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation."

Evaluation Modes

partial

Human Eval, Automatic Metrics

Includes extracted eval setup.

"Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation."

Reported Metrics

partial

Accuracy

Useful for evaluation criteria comparison.

"Filtering thus removes items on which LMs fail but humans do not, raising accuracy more than random removal."

Benchmarks and datasets

No benchmark or dataset names were extracted from the available abstract.

Reported metrics

accuracy
Human feedback details
Uses human feedback
No
Feedback types
None
Rater population
Not reported
Expertise required
Coding
Evaluation details
Evaluation modes
Human Eval, Automatic Metrics
Agentic eval
None
Quality controls
Not reported
Evidence quality
Low
Use this page as
Background context only

Research brief

Metadata summary

Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key takeaways

  • Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation.
  • We test it on BLiMP (English) and QFrBLiMP (Quebec French), the two public minimal-pair datasets releasing every forced-choice human judgment.
  • Three LLMs validate by forced choice, by rating, and as a panel, scored against human panels of one, three and five judges in two pre-registered phases.

Researcher actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended queries

Accuracy

Contribution summary

  • Minimal-pair benchmarks are only as good as their labels, and LLMs are an obvious substitute for costly human validation.
  • We test it on BLiMP (English) and QFrBLiMP (Quebec French), the two public minimal-pair datasets releasing every forced-choice human judgment.

Why it matters for eval

  • Three LLMs validate by forced choice, by rating, and as a panel, scored against human panels of one, three and five judges in two pre-registered phases.

Researcher checklist

  • Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Evaluation mode is explicit

    Detected: Human Eval, Automatic Metrics

  • Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Metric reporting is present

    Detected: accuracy